Chapter 5...Misriyah1, Hari Santoso1, Arie Bratasena1, Anas Maruf1, Elvieda Sariwati1, Vivi...
Transcript of Chapter 5...Misriyah1, Hari Santoso1, Arie Bratasena1, Anas Maruf1, Elvieda Sariwati1, Vivi...
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Chapter 5
Chapter 5 Paper 2: Avian Influenza H5N1 Transmission in
Households, Indonesia.
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Chapter 5
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Chapter 5
About this chapter
Building on the findings from Chapter 4, this chapter explored AI H5N1 transmission
patterns and risk factors for infection. Using the same data and household-study design
as that reported in Chapter 4, this study quantified zoonotic and human transmission as
well as the extent to which the virus was transmissible. Other transmission patterns
assessed include household attack rates, secondary attack rates and disease intervals
between cases in outbreaks.
This study was the first globally to assess transmission patterns for a large number of
outbreaks. It found that most H5N1-cases were a result of exposure to zoonotic sources
of virus. Strong support for human transmission of the virus was only found when a
single large cluster was included in the transmission model. The reproduction number
was well below the threshold for sustained transmission.
My role in this study was to design the study research question, extract the relevant data
from the MOH existing surveillance system and analyze the data. I worked with a
mathematical modeller to develop the disease transmission models. I wrote the paper
for publication and obtained feedback from all the co-authors. The study was submitted
to PLOS One and was published in January 2012.
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Chapter 5
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Avian Influenza H5N1 Transmission in Households,IndonesiaTjandra Y. Aditama1., Gina Samaan2*., Rita Kusriastuti1, Ondri Dwi Sampurno3, Wilfried Purba1,
Misriyah1, Hari Santoso1, Arie Bratasena1, Anas Maruf1, Elvieda Sariwati1, Vivi Setiawaty3, Kathryn
Glass2, Kamalini Lokuge2, Paul M. Kelly2,4, I. Nyoman Kandun1
1 Directorate-General Disease Control and Environmental Health, Ministry of Health, Salemba, Jakarta, Indonesia, 2 National Centre for Epidemiology and Population
Health, The Australian National University, Canberra, Australian Capital Territory, Australia, 3 National Institute of Health Research and Development, Ministry of Health,
Salemba, Jakarta, Indonesia, 4 Population Health Division, Australian Capital Territory Government Health Directorate, Canberra, Australian Capital Territory, Australia
Abstract
Background: Disease transmission patterns are needed to inform public health interventions, but remain largely unknownfor avian influenza H5N1 virus infections. A recent study on the 139 outbreaks detected in Indonesia between 2005 and2009 found that the type of exposure to sources of H5N1 virus for both the index case and their household membersimpacted the risk of additional cases in the household. This study describes the disease transmission patterns in thoseoutbreak households.
Methodology/Principal Findings: We compared cases (n = 177) and contacts (n = 496) in the 113 sporadic and 26 clusteroutbreaks detected between July 2005 and July 2009 to estimate attack rates and disease intervals. We used final sizehousehold models to fit transmission parameters to data on household size, cases and blood-related household contacts toassess the relative contribution of zoonotic and human-to-human transmission of the virus, as well as the reproductionnumber for human virus transmission. The overall household attack rate was 18.3% and secondary attack rate was 5.5%.Secondary attack rate remained stable as household size increased. The mean interval between onset of subsequent casesin outbreaks was 5.6 days. The transmission model found that human transmission was very rare, with a reproductionnumber between 0.1 and 0.25, and the upper confidence bounds below 0.4. Transmission model fit was best when thedenominator population was restricted to blood-related household contacts of index cases.
Conclusions/Significance: The study only found strong support for human transmission of the virus when a single largecluster was included in the transmission model. The reproduction number was well below the threshold for sustainedtransmission. This study provides baseline information on the transmission dynamics for the current zoonotic virus and canbe used to detect and define signatures of a virus with increasing capacity for human-to-human transmission.
Citation: Aditama TY, Samaan G, Kusriastuti R, Sampurno OD, Purba W, et al. (2012) Avian Influenza H5N1 Transmission in Households, Indonesia. PLoS ONE 7(1):e29971. doi:10.1371/journal.pone.0029971
Editor: Leo L. M. Poon, University of Hong Kong, Hong Kong
Received September 6, 2011; Accepted December 9, 2011; Published January 4, 2012
Copyright: � 2012 Aditama et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: [email protected]
. These authors contributed equally to this work.
Introduction
The avian influenza (AI) H5N1 virus remains of international
public health concern due to its pandemic potential. Based on
analyses of AI H5N1 outbreaks during 2003 to 2009, most cases
were sporadic and had documented exposure to zoonotic sources
of the virus [1]. For clusters of AI H5N1 infection, the majority
occurred in people who were genetically related to each other and
most also had exposure to zoonotic (bird to human) sources of
virus [1]. Studies suggest that human transmission of the virus
occurred in a very limited way in some clusters [2,3]. However, the
transmission patterns remain largely unknown.
Quantification of transmission patterns such as the probability
of both human and zoonotic transmission of the H5N1-virus, the
reproduction number (R0), secondary attack rates (SAR) and the
interval between case onsets are important parameters to inform
preparedness and response measures to outbreaks, especially to
signal events that indicate changed virus behavior [4]. It is also
crucial that both zoonotic and human infection pathways are
considered, and results are interpreted in the context of a zoonotic
infection with limited transmission among humans [5,6]. Models
that incorporate both the zoonotic and human transmission
components are rare [5].
As of July 30, 2009, Indonesia had reported 139 outbreaks of
avian influenza (AI) H5N1 infection in humans with a case fatality
rate of 85% [7]. The epidemiology of Indonesia’s cases has been
reported previously [8–10]. A recent study on the 139 outbreaks
assessed the risk factors for household clustering of cases and the
risk factors for who in the household is likely to become a
secondary case of H5N1-infection [11]. The study found that the
type of exposure to sources of H5N1 for both the index case and
their household members impacted the risk of additional cases in
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the household. The study also added evidence that H5N1 infection
may be dependent on host genetic susceptibility since first-degree
blood relatives to index cases were at greater risk of becoming
secondary cases. However, this study did not assess the attack rates
(AR), SAR or transmission parameters in those outbreak
households.
To date, only one study has estimated the transmission patterns
based on case data in Indonesia [4]. Estimates generated were
solely based on one outbreak – a cluster of one probable and seven
confirmed cases detected in North Sumatra in 2006. The study
found statistical evidence of human-to-human transmission and
estimated SAR at 29% and R0 at 1.14 [4]. Since data on the total
persons exposed and individual factors such as exposure type were
not fully available to that study, the transmission pathways were
not investigated in detail. Also, since the model was fitted and
transmission estimates generated based only on that one cluster,
which is considered atypical due to its large size, the estimates are
likely to be an over-estimate for outbreaks in Indonesia.
Since Indonesia’s cumulative AI H5N1 infection case count
represents one-third of the world’s cases, the outbreak transmission
patterns are of international importance. Building on previous
findings about the epidemiology of H5N1 infection in households
[11], we describe infection AR, infection SAR, risk factors for
H5N1 infection and intervals between case illness onsets. We then
estimate transmission parameters and quantify the relative
contribution of zoonotic and human transmission as well as the
extent to which the virus was transmissible between people
(reproduction number). While international data suggest most
transmission is zoonotic, there is also some evidence of human-to-
human transmission [2,12]. We fitted household models to the
Indonesian data that allow for both zoonotic and human-to-
human transmission to assess the extent of transmission from each
source and to provide an estimate of the reproduction number in
the case that human-to-human transmission occurs.
Results
A total of 139 outbreaks of human AI H5N1 infection were
detected in Indonesia in the four year study period. There were
113 sporadic case outbreaks and 26 cluster outbreaks. The total
number of cases was 177, with 64 cases in the 26 clusters. Only
one cluster had over four cases; the North Sumatran cluster of
2006, which can be considered an outlier based on its large size of
seven confirmed and one probable case. There were 535
household contacts to index cases in the study, of which blood
relation was known for 94% (n = 503). Most of the 503 contacts
were blood relatives (n = 383, 76%) and 120 (24%) were non-blood
relatives. None of the non-blood related household contacts
became secondary cases.
Household StudyFor the 80 outbreaks for which household and contact data
were available, the proportion of cluster to sporadic outbreaks
increased as household size increased (Table 1). To highlight the
impact of the outlier cluster on the AR and SAR, findings are
presented both including and excluding that cluster. The overall
AR was 17.8% (103 cases / 579 exposed) when the outlier cluster
was excluded and 18.3% (111 cases / 607 exposed) when included.
There was a stable SAR between 3.1–4.5% across household size
(Table 1). However, inclusion of the outlier cluster inflated SAR
for households with.15 persons to 12.5% (Table 1). Thesefindings are consistent with predominantly zoonotic virus
transmission. In the absence of human transmission, and with
low levels of zoonotic transmission, the AR would be expected to
decline with household size, while the SAR should remain roughly
constant.
Cases (nz = 177) and healthy contacts (n = 496) were compared
to assess risk factors for infection (Table 2). Young age groups (#30 years) were at increased risk of infection, where individuals
between five and 17 years of age had 3.5 times the odds to be
infected when compared with those .30 years of age [AdjustedOdds Ratio (aOR) = 3.44, 95% Confidence Interval (CI)) 1.86–
6.36]. Most cases (87%) and their healthy contacts (69%) had
zoonotic exposure. However, direct exposure to zoonotic sources
of AI H5N1 virus tripled the odds of infection (aOR = 3.08, 95%
CI 1.54–6.13). Lastly, small households (1–5 persons) were
significantly more likely to have cases than households with .5people (Table 2). The final multivariate model with three variables
had good fit (p = 0?17).In cluster outbreaks, the median interval between the index case
onset and secondary case onset of illness was 8 days (range 1–21
days, Figure 1A). The median interval between the onset of illness
of a secondary case and the previous case in the same outbreak
was 6 days (range 1–12 days, Figure 1B). Based on the
investigation reports, eleven secondary cases had inconclusive
exposure to a zoonotic source of virus. All of these had onset of
illness at least two days after the index case’s onset of illness. For
these 11 cases, the median interval between illness onset of serial
cases was 8 days (range 2–11 days, Figure 1B).
Table 1. Household size and secondary attack rate for outbreaks of avian influenza H5N1 infection.
Contact dataHouseholdsize Outbreak size (confirmed and probable cases)
Totaloutbreaks
Proportioncluster
Totalcontacts
Secondarycases SAR
1 2 3 4 5 6 7 8
Available 1–5 27 4 1 0 0 0 0 0 32 0.16 152 9 0.059
6–10 25 6 2 0 0 0 0 0 35 0.24 219 8 0.036
11–15 8 3 0 1 0 0 0 0 12 0.33 85 3 0.035
.15 0 2 0 0 0 0 0 1 3 1.00 69 9 0.130a
Sub-total 60 15 3 1 0 0 0 1 80 0.24 525 29 0.055b
Not available 53 5 1 0 0 0 0 0 59 0.10 - 6 -
Total 113 20 4 1 0 0 0 1 139 0.19 - 35 -
aSAR declines to 0.047 when outlier cluster is excluded.bSAR declines to 0.044 when outlier cluster is excluded.doi:10.1371/journal.pone.0029971.t001
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Table 2. Comparison of cases (n = 177) and healthy contacts (n = 496) in outbreaks of avian influenza H5N1 infection.
Univariate Multivariate a
Variable Cases, n (%)Healthycontacts, n (%)
OR(P-value)
Adjusted OR(P-value) 95% CI
Age groups (years) b
0–4 18 (10) 41 (9) 2.66 (0.004) 3.18 (0.004) 1.45–6.98
5–17 65 (37) 96 (21) 4.11 (,0.001) 3.44 (,0.001) 1.86–6.36
18–30 61 (35) 125 (28) 2.96 (,0.001) 3.20 (,0.001) 1.81–5.68
.30 31 (18) 188 (42) Reference group - -
Sex
Male 83(47) 225 (47) 0.99 (0.94)
Female 94 (53) 258 (53)
Exposure
Direct zoonotic 81 (46) 130 (26) 4.02 (0.002) 3.08 (0.001) 1.54–6.13
Indirect zoonotic 72 (41) 211 (43) 2.20 (,0.001) 1.43 (0.29) 0.72–2.81
Inconclusive zoonotic 24 (13) 155 (31) Reference group - -
Household size (persons) c
1–5 51 (46) 143 (29) Reference group - -
6–10 38 (34) 211 (43) 0.51 (0.009) 0.50 (,0.001) 0.34–0.73
11–15 10 (9) 82 (16) 0.35 (0.001) 0.32 (,0.001) 0.18–0.57
.15 12 (11) 60 (12) 0.51 (0.07) 0.40 (0.16) 0.11–1.43
aObservations = 561, Goodness-of-fit test: P = 0.17, OR denotes odds ratio, CI denotes confidence interval. OR were adjusted for the inclusion of the three variables inthe final multivariate model.
bData missing for two cases and 46 healthy contacts.cData missing for 66 cases from the 59 outbreaks for which household data were not available.doi:10.1371/journal.pone.0029971.t002
Figure 1. Interval between onset of illness for cases (n = 34) in outbreaks of avian influenza H5N1 infection. Panel A shows the intervalbetween onsets of illness of index and secondary cases in outbreaks. Panel B shows the interval between onsets of illness of serial cases in outbreaks.Black denotes cases not exposed to zoonotic sources of virus and white denotes cases exposed to zoonotic sources of virus.doi:10.1371/journal.pone.0029971.g001
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Transmission ModelTo assess the exposure of secondary cases, Table 3 presents the
transmission analysis comparing three model types: all transmis-
sion from zoonotic sources (Model A), all transmission was human
transmission (Model B) and transmission was from both zoonotic
and human sources (Model C). Two denominator populations are
presented for comparison; all exposed individuals in outbreaks and
all exposed blood-related household members to index cases. The
final column of the tables shows the percentage support for the
models, which can be interpreted as the probability that the model
is the best among those considered. To highlight the impact of the
outlier cluster on transmission parameters and model selection,
findings for two datasets are presented; one with the outlier cluster
included and one with it excluded.
Regardless of the denominator population or the dataset, there
was much less support for Model A (zoonotic transmission only)
than either Models B (human transmission only) or C (combina-
tion of zoonotic and human transmission) (Table 3). This was
confirmed by a simulation-based test of model fit, which
demonstrated significant differences between Model A and the
data (p,0.01 for both). Despite significant evidence that humantransmission occurred when the outlier cluster was included in the
analysis, estimated human transmission rates were low with the
reproduction number lying between 0.1 and 0.25, and the upper
confidence bounds all below 0.4 for an exposed population of five
individuals. Estimated zoonotic transmission rates ranged from 0
to 0.38 cases in an exposed population of five household members.
When the analysis excluded the outlier cluster (Table 3), similar
estimates for the human transmission parameters and the
reproduction number were found, but there was no longer
significant evidence of human transmission. Indeed, the model
with the strongest support was Model A (zoonotic transmission
only), with 0.31 zoonotic cases infected in an exposed population
of five household members. This suggests that the main evidence
for human transmission comes from the outlier cluster. For all
model types, both including and excluding the outlier cluster, use
of blood-related household members as the denominator popula-
tion provided better model fit. A test of the sensitivity of our results
to the households in which contact data were missing found very
little change to the transmission estimates, with estimates of
zoonotic transmission parameters reduced by around 0.05–0.1
cases in an exposed population of size 5, point estimates of human
transmission parameters largely unchanged, and a decrease in the
upper bound of the human transmission parameter of 0.02–0.08
cases in an exposed population of size 5.
Discussion
This study is the first globally to examine AI H5N1 transmission
patterns in households for a large number of outbreaks aimed at
quantifying human-to-human transmission of the AI H5N1 virus.
The study had three main findings. Firstly, most cases of AI H5N1
infection were a result of exposure to zoonotic sources of virus. In
fact, the study only found strong support for human transmission
of the virus when a single large cluster was included in the
transmission model. Secondly, the overall SAR was 5.5% in the 80
outbreaks for which household contact data were available. This
was much lower than previous estimates [4]. Thirdly, the study
adds evidence that blood relatives are at greatest risk of becoming
secondary cases in outbreak households. This adds support to the
hypothesis that there is an element of genetic susceptibility to AI
H5N1 infection [3].
The finding that the AI H5N1 virus does not transmit efficiently
between humans and that infection remains primarily zoonotic
impacts the interpretation of the interval between case onsets and
the SAR. These parameters should not be interpreted as human-
to-human transmission parameters. Rather, the interval between
case onsets (median 6 days, range 1–12 days) represents observed
timelines between human cases during an epizootic and indicates
Table 3. Transmission parameters for outbreaks of avian influenza H5N1 infection.
DataDenominatorpopulation Model description
Mean human transmissioncases a (95% CI)
Mean zoonotic infectedcases b (95% CI)
AICC percentsupport c
80 outbreaks (NorthSumatra clusterincluded)
All exposedindividuals
A) Only zoonotic transmission - 0.276 (0.126, 0.476) 0.1
B) Only human transmission 0.172 (0.026, 0.322) - 6.3
C) Full model 0.115 (0.009, 0.315) 0.094 (0.000, 0.344) 4.4
All exposed blood-relatives
A) Only zoonotic transmission - 0.385 (0.185, 0.635) 2.2
B) Only human transmission 0.231 (0.082, 0.382) - 42.9
C) Full model 0.140 (0.004, 0.390) 0.157 (0.000, 0.452) 44.0
79 outbreaks (NorthSumatra clusterexcluded)
All exposedindividuals
A) Only zoonotic transmission - 0.221 (0.071, 0.421) 5.1
B) Only human transmission 0.166 (0.024, 0.316) - 2.4
C) Full model 0.052 (0.000, 0.302) 0.158 (0.000, 0.403) 2.5
All exposed blood-relatives
A) Only zoonotic transmission - 0.310 (0.110, 0.510) 53.3
B) Only human transmission 0.227 (0.077, 0.427) - 14.0
C) Full model 0.052 (0.000, 0.352) 0.242 (0.000, 0.542) 22.7
aMean number of secondary cases infected by a single index case in an exposed population of size 5, CI denotes confidence interval.bMean number of zoonotic cases in an exposed population of size 5.cAICC denotes Akaike Information Criterion adjusted for small sample size. This indicates the percent probability that the model is the best amongst those considered.doi:10.1371/journal.pone.0029971.t003
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the duration of risk of more cases being detected in association
with the epizootic event. This information can guide the length of
contact tracing needed to detect and prevent further cases during
an outbreak. The findings from this study reinforce the WHO
recommendation to trace and monitor case contacts for two weeks
after the illness onset of the last case [13].
The SAR results add to the body of knowledge on typical
outbreak size associated with the current zoonotic virus, where
SAR remained approximately stable with household size. This
provides important baseline information for future outbreak
investigations and may help in the detection of changes in virus
behavior. For a virus on the verge of efficient human spread, the
household SAR should be compared to the current findings as well
as SAR for other influenza viruses.
Although the SAR remained stable with household size, the
proportion of outbreaks with more than one case increased with
household size. This highlights an important distinction between
individual and household risk for infection with the current
zoonotic virus: a person in a large household is less likely to be
infected than a person in a small household, but large households
are more likely to have a secondary case than small households.
Whether SAR was low due to virus and host characteristics or due
to public health interventions such as prophylaxis of case contacts
or isolation of cases was not explored in this study, but warrants
future investigation. Importantly, the SAR could not be calculated
for the remaining 59 outbreaks as contact data were not available
to determine the household size. The missing data highlight the
challenge in standardizing data collection for a new emerging
disease. However, as the excluded outbreaks were typically smaller
than those with full contact data (90% of excluded outbreaks were
sporadic), it seems unlikely that inclusion of those outbreaks would
increase the overall SAR or the transmission parameters. Our
sensitivity analysis suggested that inclusion of these data would
likely result in a slight decrease in the zoonotic transmission
parameter, negligible impact on the point estimate of the human
transmission parameter, and a slight decrease in the upper bound
of the human transmission parameter.
Due to the limited sensitivity of public health surveillance
systems, varied health-seeking behavior within the population and
the potential for mild infections, it is possible that cases or clusters
of H5N1 infection were missed and not included in the analysis.
This affects our findings. If sporadic cases of H5N1 infection
resulting from zoonotic transmission of the virus were missed, then
our study likely over-estimates overall SAR and transmission
parameters. If clusters of cases were missed, then our study may
under-estimate these parameters. We speculate, based on our
H5N1 case investigations, that clusters of disease are less likely to
be missed than sporadic cases of infection since families and
healthcare workers would raise alarms in the public health system
about multiple cases of pneumonia in a single household. For mild
cases, it is feasible that cases are missed, which suggests that our
results would under-estimate transmission parameters. However,
based on studies conducted amongst poultry workers exposed to
H5N1 virus in the course of their work, mild and subclinical
infections have been limited [14–16]. This is also mirrored in
influenza virological surveillance findings conducted by countries
affected by H5N1 virus such as Lao PDR, China and Cambodia,
whereby these sentinel surveillance systems regularly detect
seasonal influenza viruses circulating in the community and in
hospital settings, yet they rarely detect cases of H5N1 virus
infection [17–19].
The disease transmission model achieved a better fit when the
exposed population was restricted to blood-related household
members. The study also found that only blood relatives to the
index case developed illness and that none of the 120 non-blood
related household members (such as spouses and family-in-law)
developed illness. Collectively, these findings add evidence to the
hypothesis that there is a host genetic effect on susceptibility to AI
H5N1 infection [11]. However, since genetic relationship and
household membership are correlated, it is difficult to identify the
mechanisms most responsible for household clustering. Thus,
further research is needed to explore these findings.
Individuals at most risk of infection were those #30 years,especially children between five and 17 years. The young age
pattern was also observed globally based on analysis of cases from
11 countries [1]. This suggests that young age groups have greater
susceptibility to AI H5N1 infection; be it due to social, hygienic or
biological factors. Potential reasons include that children are more
likely to handle sick and infected birds or to be exposed to
contaminated environment through play or through bird rearing.
In Indonesia, anecdotal evidence suggests that bird rearing is
delegated to young household members. Children are less
conscious of hygiene and thus may have had unprotected
interaction with sources of virus [20].
Household based studies exploring risk factors for infection are
less likely to be affected by case-ascertainment bias [21]. However,
since household data were not available for all outbreaks, our
analyses and conclusions were based on a restricted dataset and
should be interpreted with caution as the missing data limit the
power of our study. Nevertheless, as discussed earlier, since 90%
outbreaks lacking household data only had one case, our study
likely over-estimated the transmission parameters and the SAR,
indicating that human transmission rates were very low.
Overall, the study found that AI H5N1 human infection
resulting from human transmission of the virus was very limited,
and that the reproduction number was well below the threshold
for sustained transmission. Case clustering does not always denote
human transmission of the virus, but is often the result of
household members’ shared exposure to zoonotic sources of the
virus [22]. The study findings also suggest that there may be a host
genetic effect on susceptibility to infection, but this warrants
further investigation through epidemiological and immunological
studies to untangle the correlation between household member-
ship, shared exposures and genetics.
Materials and Methods
Ethics StatementAll data in this study were obtained from the case-investigation
reports and the surveillance database at the Ministry of Health,
which were collected as part of an ongoing public-health
investigation. Permission to conduct the study and analyze the
data was obtained from the data custodian (first author, Director-
General for Disease Control and Environmental Health at the
Ministry of Health, Republic of Indonesia). Data shared with
international study collaborators, who were not involved in the
case investigations, were de-identified to protect the confidentiality
of the cases and their families, whereby names and addresses were
removed. Ethics approval for the study was obtained from the
Australian National University’s Human Research Ethics Com-
mittee.
SettingThe Ministry of Health AI H5N1 case database and detailed
case investigation forms were reviewed and analyzed for cases
detected in Indonesia between July 2005 and July 2009. The study
conformed with the WHO definitions [13], whereby a cluster is a
group composed of one confirmed case of H5N1 virus infection
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and additional confirmed or probable cases associated with a
specific setting, with the onset of cases occurring within 2 weeks of
each other. In households with a cluster of cases, the index case
was defined as the one with the earliest symptom onset date
amongst all the cases in that household. A sporadic outbreak was
defined as one confirmed case of H5N1 virus infection. Case
definitions for probable and confirmed cases were based on the
WHO definitions described previously [23]. For both sporadic and
cluster outbreaks, a household contact was a person who had at
least four hours contact with a probable or confirmed case at home
within the seven days prior or 14 days after the case’s onset of
illness.
Data CollectionField investigation teams investigated every outbreak. Teams
interviewed cases when possible (since many cases died before
investigation teams arrived), family members and key informants
such as healthcare workers. As described previously [8,11], data
were collected using a standardized H5N1-case questionnaire
developed by the Ministry of Health based on WHO guidance
[24]. The questionnaire collected data on the case’s household,
clinical symptoms, healthcare facility attendance and potential
zoonotic, human and environmental exposures to sources of
H5N1-virus. Medical records from all healthcare facilities visited
by cases during the course of their illness were reviewed and
extracted to complete the questionnaire.
Contact tracing, clinical examination and testing of household
contacts were done during the investigation. Serum samples were
collected from all healthy household contacts to assess for H5N1
seroconversion using microneutralization test or haemagglutina-
tion inhibition test (with horse red blood cells). For household
contacts with symptoms of H5N1 infection, nasal and throat swabs
were collected and tested using real-time reverse transcriptase
polymerase chain reaction (RT-PCR) test. All tests were
conducted according to the WHO guideline on recommended
laboratory procedures for H5N1 detection [25]. Healthcare
workers from the nearest government primary healthcare centre
were instructed to visit the household daily for two weeks to
monitor and detect any additional cases.
Household StudyAR, SAR, risk factors for infection and intervals between case
onsets were analyzed in a household-based study. Household size
was the number of people in the household including cases. A
household contact was a person who had at least four hours
contact with a case at home within the seven days prior or fourteen
days after a case’s onset of illness. AR was calculated for the 80
outbreaks (60 sporadic and 20 clusters) out of the 139 for which
household data were available. Data on household contacts were
missing for 59 outbreaks, of which 90% (n = 53) were sporadic case
outbreaks and the largest outbreak involved three cases. AR was
defined as the proportion of people who met the definition for
confirmed or probable AI H5N1 infection in the outbreak
(household). SAR was defined as the proportion of household
contacts who met the probable or confirmed case definition after
the onset date for the index case and within two weeks of the onset
of symptoms of a prior household case. Two weeks was selected as
the maximum follow up period based on WHO guidance [13].
The intervals (days) between onset of symptoms of index cases and
subsequent cases in clusters, and the interval between serial cases
in clusters were calculated.
Logistic regression models that accounted for household
clustering using a cluster robust standard error for the coefficients
were used to evaluate the risk factors for infection. Multivariate
models were constructed using variables significant at p = 0.1 in
the univariate analyses. A final model was achieved by sequentially
discarding terms not significant at P = 0.05 starting with the ones
with the highest P-values. We used the le Cessie-van Houwelingen-
Copas-Hosmer unweighted sum of squares goodness-of-fit test to
assess model validity, as advocated by Hosmer et al. [26–28]. Stata
software version 10.0 (StataCorp) was used for this analysis.
Four variables were explored as risk factors for infection: age,
sex, exposure type and household size. To simplify interpretation
of results, age and household size were analyzed categorically.
Categories were based on data spread; four groups for age in years
(0–4, 5–17, 18–30 and $31) and four groups for household size(1–5, 6–10, 11–15 and .15 people). Exposure was defined aswhether the individual had direct, indirect or inconclusive
zoonotic exposure to a source of AI H5N1 virus. Direct zoonotic
exposure referred to cases who handled sick or dead poultry,
handled poultry products such as fertilizers, or who had poultry
deaths in the home. Indirect zoonotic exposure referred to cases
where poultry deaths were reported in the neighborhood, cases
where healthy poultry were present in the neighborhood and cases
who visited live bird markets. Inconclusive zoonotic exposure
refers to cases where no zoonotic source of infection could be
found despite investigation.
Transmission ModelTo assess the potential for human transmission of the virus, we
used final size household models to fit the human and zoonotic
transmission parameters to outbreak data (household size, number
of cases, blood-related household members to index case) in a
manner similar to that described in van Boven et al. [29]. This
approach allows for both human and zoonotic transmission, and
enables comparison of different transmission assumptions. We
used Akaike Information Criterion adjusted for small sample size
(AICC) to select the most appropriate models. The AICC percent
support gives the probability that the model is the best model of
those considered, but does not indicate how well the suite of
models fit the data [30]. We used a simulation-based approach to
compare the data with each of the model predictions, which
allowed us to identify those models that differed significantly
(P,0.05) from the data. Matlab (version R2010b) was used for thisanalysis. Our preliminary analysis indicated that density-depen-
dent transmission [31] gave a better fit to the data than frequency-
dependent transmission [29], and that assumptions concerning the
distribution of the infectious period did not affect our results. Thus,
our detailed analysis used a model with a fixed infectious period
and density-dependent transmission. Under these assumptions,
outbreak sizes will vary according to the exposed population, and
we present results for an exposed population of size five (the
median household size in the data).
Our estimation methods calculated the best-fit parameters to
cluster data consisting of the number of exposed individuals, the
number of index cases and the final outbreak size. In our initial
analysis, we used all individuals exposed for a period of four hours
or more in the household as the exposed population. In light of
evidence concerning transmission of H5N1 to blood-related
contacts [1,3], we also considered an alternative analysis in which
the exposed population was restricted to all blood relatives exposed
for a period of four hours or more in the household. Finally, we
tested the sensitivity of our results to the inclusion of those
households for which contact data were missing, by including the
missing households into the data, assuming that they had 5
household members (the median household size in the data) and 4
blood relative contacts (again, the median in the data).
H5N1 Transmission Patterns
PLoS ONE | www.plosone.org 6 January 2012 | Volume 7 | Issue 1 | e29971
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Acknowledgments
We would like to acknowledge the support of the provincial and district
health offices, the Ministry of Agriculture and laboratory staff in the
outbreak investigation and data collection. We thank Alex Richard Cook
for input on the statistical analyses in the study.
Author Contributions
Conceived and designed the experiments: TYA GS KG KL PMK INK.
Performed the experiments: GS RK WP M HS AB. Analyzed the data: GS
AM ES VS KG. Contributed reagents/materials/analysis tools: HS VS
ODS. Wrote the paper: GS TYA KG KL PMK.
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26. le Cessie S, van Houwelingen J (1991) A goodness-of-fit test for binary regression
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28. Hosmer D, Hosmer T, Le Cessie S, Lemeshow S (1997) A comparison of
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29. van Boven M, Koopmans M, Du Ry van Beest Holle M, Meijer A,
Klinkenberg D, et al. (2007) Detecting emerging transmissibility of avian
influenza virus in human households. PLoS Comput Biol 3: e145.
30. Burnham K, Anderson D (2002) Model selection and multimodel inference.
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31. Begon M, Bennett M, Bowers RG, French NP, Hazel SM, et al. (2002) A
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Chapter 5
74
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Chapter 6 Paper 3: Chicken faeces garden fertilizer: possible source of
human avian influenza H5N1 infection.
75
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Chapter 6
76
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Chapter 6
About this chapter
Chapters 4 and 5 explored the epidemiology of AI H5N1 in Indonesia based on the
outbreak investigation reports and surveillance dataset at MOH. As part of the data used
to explore the disease epidemiology, this chapter describes one specific cluster from the
surveillance dataset. The cluster was detected in Indonesia in 2005 and comprises two
cases: a 37 year old female and a nine year old male. The cluster is presented as a case
report.
This case report is of importance as it was the first report globally to explore poultry
faeces in garden fertilizer as a potential source for AI H5N1 human infection. The
epidemiological investigation and supporting laboratory findings are presented. Even
though the virological evidence does not provide conclusive evidence that the isolate
from the H5N1-contaminated garden fertilizer was the source of the index case’s
infection, it does highlight the importance of environmental investigation and laboratory
testing to identify putative sources of infection for this emerging infectious disease.
I participated in the investigation and data collection for the outbreak reported in this
chapter. In addition to collecting and analyzing the epidemiological data, I also
coordinated with the virologists nationally and at a WHO Collaborating Centre for the
virological analyses. I wrote the paper and obtained feedback from all the co-authors
pre-publication. The published study has been reproduced here with permission from
the publisher, John Wiley and Sons.
77
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Chapter 6
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ORIGINAL ARTICLE
Chicken Faeces Garden Fertilizer: Possible Source of HumanAvian Influenza H5N1 InfectionI. N. Kandun1, G. Samaan2,3, S. Harun4, W. H. Purba1, E. Sariwati1, C. Septiawati1, M. Silitonga2,N. P. I. Dharmayanti5, P. M. Kelly3 and T. Wandra1
1 Directorate General Disease Control & Environmental Health, Ministry of Health, Jakarta, Indonesia2 World Health Organization, Jakarta, Indonesia3 National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia4 National Institute of Health Research and Development, Ministry of Health, Jakarta, Indonesia5 Indonesian Research Center for Veterinary Science, Jakarta, Indonesia
Impacts
• Avian influenza A H5N1 infection in humans is generally associated withclose contact with infected birds.
• This paper points to poultry faeces used in garden fertilizer as a potentialsource for human infection with avian influenza A H5N1 virus.
• Public education is needed to ensure safe handling of poultry faeces,especially in areas affected avian influenza A H5N1.
Introduction
Indonesia detected its first human case of avian influenza
H5N1 in June 2005; nearly 2 years after the disease was
detected in the local poultry population (World Health
Organization, 2005). By end of June 2008, Indonesia had
confirmed 135 human cases of H5N1, of which 110 were
fatal (case fatality rate = 81%) (World Health Organiza-
tion, 2008).
Each human case of H5N1 in Indonesia is thoroughly
investigated by an epidemiological team to determine the
likely source of infection and to determine whether the
outbreak led to further human cases. This case report
describes a confirmed human case where the investigation
found that chicken faeces used as garden fertilizer was
contaminated with viable H5N1 virus. We hope that this
report will prompt investigators worldwide to consider
this as a possible source of infection for H5N1 human
cases and to incorporate testing of poultry products into
their investigations.
Case Descriptions
A 37-year-old female from South Jakarta in the western
part of Java developed fever and sore throat on 31 August
2005. She consulted a local medical clinic 3 days later
because of persistent symptoms. By 6 September, she had
also developed cough, shortness of breath and difficulty
breathing and was admitted to a private hospital. Based
on the clinical presentation, the hospital suspected influ-
enza A H5N1 infection as a differential diagnosis. Throat
and nasal swabs, as well as blood samples, were collected
from the patient on 6 and 9 September. She developed
respiratory distress and died on 10 September 2005.
Keywords:
Avian influenza H5N1; exposure; Indonesia,
chicken; fertilizer; transmission
Correspondence:
Toni Wandra. Ministry of Health, Indonesia,
Jalan Percetakan Negara No. 29, Salemba,
Jakarta Pusat 10560, Indonesia.
Tel.: +62-21-4201255; Fax: 62-21-4201255;
E-mail: [email protected]
Received for publication August 12, 2008
doi: 10.1111/j.1863-2378.2009.01246.x
Summary
Avian influenza H5N1 infection in humans is typically associated with close
contact with infected poultry or other infected avian species. We report on
human cases of H5N1 infection in Indonesia where exposure to H5N1-infected
animals could not be established, but where the investigation found chicken
faeces contaminated with viable H5N1 virus in the garden fertilizer. Human
cases of avian influenza H5N1 warrant extensive investigations to determine
likely sources of illness and to minimize risk to others. Authorities should reg-
ulate the sale and transportation of chicken faeces as fertilizer from areas where
H5N1 outbreaks are reported.
Zoonoses and Public Health
ª 2009 Blackwell Verlag GmbH • Zoonoses Public Health. 1
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Samples tested positive for H5N1 by RT-PCR on 11 Sep-
tember at the National Institutes of Health, Research and
Development, and the results were confirmed on 16 Sep-
tember by two different WHO H5 reference laboratories
(World Health Organization, 2006). A virus was isolated
and sequenced, where it was found to be of a purely
avian source.
A 9-year-old male, a nephew and blood-relative of the
37-year-old patient, developed headache and fever (40�C)on 4 September; 4 days after the onset of symptoms for
the 37-year-old patient. A mild cough began 2 days later.
He presented to a doctor on days 6 and 7 of illness due
to his persistent symptoms. On 10 September, he was
prescribed symptomatic treatments and antibiotics, but
no antiviral drugs, as doctors did not suspect H5N1
infection. He did not develop shortness of breath or dis-
abling symptoms. His white cell and platelet counts were
8800 cells/mm3 and 130 000 cells/mm3 respectively on 10
September, and 7100 cells/mm3 and 270 000 cells/mm3
respectively on 11 September.
As part of the contact tracing conducted for the 37-
year-old index patient, sera and throat/nasal swabs were
collected from the boy on 13 September. As the boy was
mildly ill and had contact with a confirmed case of avian
influenza, he was referred to hospital to be managed as a
suspected H5N1 case. He was administered oseltamivir
and remained under observation in the hospital even
though his symptoms had subsided. Throat swabs col-
lected on 17 September and 20 September from the boy
tested positive by RT-PCR for H5N1, but a virus could
not be isolated for sequencing. Samples collected after 20
September were found to be negative for the virus. Acute
and convalescent sera were collected from the patient on
13 September and 7 October 2005, respectively. A micro-
neutralization titre of 1 : 20 was observed. He survived
and was discharged on 26 September.
Epidemiological Investigation
Family members were interviewed to assess exposure his-
tory, to cross-check timelines and to determine the mode
of disease transmission for the cases, and serum samples
were collected from relatives to assess serological evidence
of H5N1 infection (below).
The index patient (37-year-old female) managed the
family’s small printing business. The husband did not
identify any occasions in the 2 weeks prior to the onset
date where the patient slaughtered chicken or came into
direct contact with sick animals. She also had not trav-
elled to areas known to be infected with avian influenza
nor was she in close contact with a person with influ-
enza-like illness or acute respiratory illness. The husband
reported that the patient never visited wet markets,
preferring to purchase eggs and chicken pieces from the
food stalls near her workplace and often purchased ready-
to-eat fried chicken. The patient was known to be a keen
gardener, and she used to purchase garden fertilizer
(chicken faeces) in sealed bags from a gardening shop to
use for her potted plants. She did not wear gloves or a
mask whilst gardening.
The home and neighbourhood of the index patient
were thoroughly inspected to assess the possible source of
infection. The 37-year-old patient only kept pet fish and
regularly purchased worms for them from a local pet
shop. The pet shop owner reported no bird deaths in the
2 weeks preceding the index patient’s onset of illness. A
bag of chicken faeces garden fertilizer was found at the
patient’s home. The husband stated that the bag was pur-
chased before the patient’s onset, but the exact dates of
purchase or manufacture were unknown. The label on the
bag indicated that the fertilizer came from a company
in East Java (over 1000 km away). Through trace-back,
we found out that the company purchased chicken
faeces from a variety of collectors for inclusion in their
product, and that the collectors sourced the faeces from
numerous farms, as far as hundreds of kilometres away.
Further trace back was deemed unreliable and was not
conducted.
There were many caged birds, chickens and some
swans in the index patient’s neighbourhood, but no
reported deaths. There was also a backyard poultry
slaughterhouse approximately 50 metres away from her
home. The slaughterhouse was not located on the same
road as the patient’s home, and could only be reached by
entering small side lanes. The slaughterhouse did not keep
cages or chicken flocks but received approximately 150
chickens from a neighbouring district to slaughter on a
daily basis. Most slaughtered chickens were sold to the
wet market 2 km from the patient’s home. It is unlikely
that the patient had contact with these chickens as they
generally arrived and were slaughtered before dawn to be
available at the markets by sunrise. The patient did not
purchase chicken meat from this slaughterhouse nor the
wet market.
The 9-year-old nephew did not report any contact with
chicken or birds in the fortnight preceding his illness.
Yet, he visited the 37-year-old index-patient in her home
2 days into her illness (1 September). They met again at a
large family gathering the next day.
Laboratory Investigation
Serum samples were collected from 119 contacts traced
during the investigation to assess for H5N1 seroconver-
sion. Samples were collected from 49 family members, 26
neighbours, 41 healthcare workers and 3 gravediggers. All
H5N1 Contaminated Fertilizer I. N. Kandun et al.
2 Blackwell Verlag GmbH • Zoonoses Public Health.
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samples were collected at least 2 weeks after the index
patient’s illness onset. The samples were tested by micro-
neutralization assay, where none was positive for H5N1
antibodies. Nasal and throat swabs were collected from
two individuals who reported recent history of influenza-
like illness. None of these contacts was found to be
infected with H5N1. All virus isolation, RT-PCR and
microneutralization tests were carried out as described
previously (Kandun et al., 2006).
Through the course of the investigation, 99 animal and
environmental samples were collected from the 37-year-
old patient’s home, relatives’ homes, nearby slaughter-
house and wet market. All of the samples were collected
17 days after the index patient’s onset of illness. This
included animal samples (chickens, caged birds, swans)
and environmental swabs (garden fertilizer from the
patient’s home and water from the processing tubs at the
slaughterhouse). All of the animals sampled appeared
healthy at the time of specimen collection. To process
environmental samples, 5 g of specimen was dissolved in
virus transport medium. The suspension was then centri-
fuged and the supernatant was inoculated into 9- to
11-old-day specific-pathogen-free embryonated eggs for
isolating virus. From the animal and environmental sam-
ples, one chicken sampled at a wet market 2 km away
from the index patient’s home was H5N1 positive by
RT-PCR, but the virus could not be isolated for sequenc-
ing. The chicken faeces (garden fertilizer) collected from
the index patient’s home also tested H5N1 positive by
RT-PCR. All other samples, including samples from the
slaughterhouse, were negative.
For both, patient and fertilizer isolates, RNA extraction,
cDNA synthesis and PCR were used as described previ-
ously (Guan et al., 2000). Sequencing was performed with
the BigDye Terminator v3.1 cycle sequencing kit on an
ABI PRISM 3700 DNA analyzer (Applied Biosystems) by
following the manufacturer’s instructions. Sequence
fragments were assembled with Lasergene (version 6.0;
DNASTAR) and then aligned by using BioEdit, version 7,
and residue analysis was performed with BioEdit, version
7. Phylogenetic trees were generated by neighbour-joining
bootstrap analysis (1,000 replicates) by using the Tamura-
Nei algorithm in MEGA, version 2.1. The percent nucleo-
tide homology of HA genes of A/Indonesia/6/05 versus
A/Indonesia/Environment/05 was calculated using Meg-
Align v8.0.2 (DNASTAR) and found to be 97.5% (Fig. 1).
Discussion
From the epidemiological aspects of this investigation, we
found that the patient had direct and unprotected contact
with the H5N1-contaminated fertilizer. Our results also
confirmed the prolonged (>2 weeks) environmental
stability of H5N1 virus in bags of chicken faeces garden
fertilizer. However, from the virological aspects of this
investigation, we could not conclude that the isolate from
the H5N1-contaminated garden fertilizer was the source
of infection for the index patient.
The phylogenetic tree highlighted that the virus iso-
lated from the index patient was most similar to viruses
isolated in the eastern parts of Java, the location of the
fertilizer manufacturing company, as opposed to isolates
located in the western parts of Java where the index
patient resided. It is possible that the fertilizer bag which
likely contained poultry faeces from many birds har-
boured multiple strains of H5N1 viruses, one of which
led to her infection. The one we isolated from the fertil-
izer bag represented a different isolate. Based on infor-
mation from the fertilizer manufacturer, it is plausible
that the same batch of fertilizer may have been contami-
nated with different strains of H5N1 virus as faeces were
sourced from various geographical areas in Java. We also
cannot exclude the possibility that the patient was
infected from other sources, such as the nearby slaugh-
terhouse or wet market, due to their geographical prox-
imity to the case. However, the interview with the
patient’s husband suggested that she did not visit these
places.
The finding of prolonged environmental stability of
H5N1 virus in chicken faeces in this observational study
is similar to observations made during the 1983–1985
Pennsylvania outbreak of avian influenza A H5N2, where
viable virus was detectable up to 6 weeks in wet faeces
(Benedictis et al., 2007). However, our finding contrasts
with some studies, where avian influenza viruses in faeces
were inactivated within a week at 25–32�C (Beard et al.,1984; Lu et al., 1984; Songserm et al., 2006). These stud-
ies suggest that sunlight and viral inactivating factors in
the organic material of faeces contribute to more rapid
inactivation of the virus. As suggested by Benedicts et al.,
survival of avian influenza viruses in faeces is likely to be
influenced by a number of factors, including the virus
strain, temperature, amount of organic material and the
host animal type. Further systematic studies need to be
conducted to assess factors that explain the conflicting
findings.
The source of the 9-year-old male’s infection could not
be ascertained. Nevertheless, he was considered epidemio-
logically-linked to the index patient. This is based on the
disease timeline, his exposure to the same contaminated
environment and close contact with the 37-year-old dur-
ing her initial phase of illness. A number of hypotheses
were considered to determine the source of the 9-year-old
patient’s infection. Exposure to a discrete source of infec-
tion at his home or at another location was possible. How-
ever, based on inspection of his home, the surrounding
I. N. Kandun et al. H5N1 Contaminated Fertilizer
ª 2009 Blackwell Verlag GmbH • Zoonoses Public Health. 3
-
neighbourhood and a general assessment of his activities
during the 2 weeks preceding the onset of illness, no inde-
pendent environmental risk factor for infection could be
identified. The second hypothesis was that he acquired
infection from a continuing point source at the index
patient’s home during his visit on 1 September. Details of
his activities during that visit, including whether he
handled the fertilizer or helped his aunt in gardening,
could not be elucidated. The third hypothesis was that the
patient acquired the infection directly from the 37-year-
old patient either on 1 or 2 September. It is not possible
to either support or discard either the second or third
hypotheses, as the patient was exposed to both risk factors
in a similar time period, and both are compatible with the
incubation period for his illness. Limited human to human
transmission could not be excluded in this cluster; how-
Ph/ST/44/04 Dk/HN/101/04
Dk/HN/5806/03 Ck/ST/4231/03
Dk/YN/6255/03 Ck/YN/115/04
Ck/IDN/4/04 Ck/IDN/2A/04
Dk/IDN/MS/04 Ck/Sragen/BPPV4/03
Ck/Ngawi/BPPV4/04 Ck/IDN/BL/03
Ck/Manggarai-NTT/BPPV6/04 Ck/Bangli-Bali/BBPV6-1/04 Ck/Kupang-2-NTT/BPPV6/04 Ck/Kupang-3-NTT/BPPV6/04 Ck/Bangli-Bali/BPPV6-2/04 Ck/Bantul/BBVet-I/2005
Ck/IDN/5/04 Ck/Jembrana/BPPV6/04
Ck/Pekalongan/BPPV4/03 Ck/Wonosobo/BPPV4/03
Ck/IDN/PA/03 Qa/Tasikmalaya/BPPV4/04
Ck/Purwakarta/BBVet-IV/04 Ck/Malang/BBVet-IV/04
Ty/Kedaton/BPPV3/04 Ck/Salatiga/BBVet-I/05
Ck/Pangkalpinang/BPPV3/04 Ck/Simalungun/BPPVI/05 Ck/Dairi/BPPVI/05
Ck/Tarutung/BPPVI/05 Ck/T-Tinggi/BPPVI/05 Ck/Deli-Serdang/BPPVI/05
Ck/Kupang-1-NTT/BPPV6/2004 IDN/6/05
Ck/Brebes/BBVET2/05 Ck/KulonProgo/BBVet-XII-1/04 Ck/KulonProgo/BBVet-XII-2/04
Qa/Boyolali/BPPV4/04 Qa/Jogjakarta/BBVet-IX/04 Ck/Jogjakarta/BBVet-IX/04
Ck/Purworejo/BBVW/05 Qa/Cirebon/BBVET1/05
Ck/Gunung-Kidal/BBVW/05 Ck/Magetan/BBVW/05
Ck/Wajo/BBVM/05 Dk/Pare-pare/BBVM/05
Dk/KulonProgo/BBVET9/04 Ck/IDN/CDC25/05 Ck/IDN/CDC24/05
IDN/175H/05 IDN/160H/05
IDN/CDC7/05 IDN/7/05
IDN/5/05 IDN/195H/05 IDN/298H/06
IDN/Environment/P3/05 IDN/245H/05
IDN/304H/06 IDN/239H/05
IDN/283H/06 IDN/292H/06
IDN/286H/06
94100
100
79
65
56
77
68
98
98
63
88
79
91
97
94
72
93
99
6462
99
99
92
68
83
58
55
62
65
54
76
0.005
Fig. 1. Phylogenetic tree of the haemaggluti-
nin of the H5N1 viruses isolated from the 37-
year-old index patient (IDN/6/05) and the
fertilizer sample found at her home (IDN/ENV-
IRONMENT/P3/05). Trees were generated with
MEGA2 by using a Tamura-Nei (gamma) neig-
hbour-joining analysis. The numbers at the
nodes indicate bootstrap values from 1000
bootstrap replicates. Accession numbers for
patient isolate and fertilizer isolate are
EU146624 and EU146642, respectively.
H5N1 Contaminated Fertilizer I. N. Kandun et al.
4 Blackwell Verlag GmbH • Zoonoses Public Health.
-
ever no further cases were identified in the course of the
investigation.
Even though the 9-year-old had clinical symptoms, and
throat swabs collected up to day 17 of his illness were
positive for H5 by RT-PCR, he did not mount a 4-fold
rise in antibody titre. A potential reason for the lack
of antibody response is the administration of Oseltami-
vir during his course of illness, however, this finding
warrants further investigation and comparison to other
surviving cases of avian influenza H5N1 infection.
Our study highlights the importance of timely and com-
prehensive environmental investigations to identify the
likely source of infection for H5N1 cases. Health authori-
ties need to devise sensitive criteria to detect suspect
human H5N1 cases, have adequate resources to investigate
them in a timely fashion and have functional coordination
with the agriculture authorities to share and analyse the
microbiological and environmental data. This is especially
important when direct contact with infected animals or
humans cannot be established, and where environmental
contamination appears to be a likely source of infection;
perhaps even a continuing source of infection. This study
suggests that avian influenza H5N1 may be acquired from
contaminated fertilizer or soil, similar to other pathogens
like Legionella longbeachae, Chlamydia psittaci and Histo-
plasma capsulatum (Heymann, 2004).
Further research is needed to learn more about the
modes of H5N1 transmission, but our findings suggest
that untreated poultry faeces as fertilizer is a possible
source of infection. Education efforts should include pub-
lic education on avoiding direct contact with items that
contain poultry sources like fertilizers, and for healthcare
providers to recognize possible sources of infection for
diagnosis. This is especially important in areas where the
virus is endemic and where access to diagnostic facilities
is limited. Figure 2 highlights some steps that can be
taken to prevent transmission of the virus from contami-
nated fertilizer to humans. In addition, control measures
should be in place to detect such contamination and
reduce future spread.
The limitations of this study are common to epidemio-
logical investigations for high case-fatality rate diseases:
the lack of primary source data from the case(s) means
that definite sources of contact cannot be elucidated.
Samples from the fertilizer producer should have been
tested for possible viruses that were similar to the cases,
but this was not possible, given the lack of batch number
on the fertilizer.
We have shown that poultry products such as fertilizer
need to be assessed in the course of an investigation, as a
possible source of transmission for H5N1. Public and
healthcare worker education should warn of the risks in
coming into unprotected contact with such products.
Acknowledgement
We are grateful to the investigators of the H5N1 influenza
outbreaks; heads and staff of the DKI Jakarta Provincial
and South Jakarta City Health Office Services; Directorate
General of Livestock, Ministry of Agriculture and
National Institute of Health Research and Development,
Ministry of Health, Indonesia for their cooperation and
technical assistance in the study. We thank JSM Peiris
and Y Guan, The University of Hong Kong for assistance
in sequencing the virus isolate and providing the
phylogenetic tree. PM Kelly is supported by Australia’s
National Health and Medical Research Council.
Conflicts of Interest
None of the authors has a commercial or other associa-
tion that might pose a conflict of interest.
Financial Sources
None.
Previous Reporting of Findings at Meetings
None.
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2. Chicken feces should be treated before sale for use as fertilizer to inactivateviruses.
3. Promote hand-hygiene and the use of masks for high-risk groups such asgardeners and farm workers handling soil, animal excreta or fertilizer.
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6 Blackwell Verlag GmbH • Zoonoses Public Health.
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Chapter 6
85
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Chapter 6
86
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Chapter 7 Paper 4: Environmental sampling for avian influenza virus
A (H5N1) in live-bird markets, Indonesia
87
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Chapter 7
88
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Chapter 7
About this chapter
Chapters 4 to 6 explored the epidemiology of AI H5N1 in Indonesia based on the
outbreak investigation reports and surveillance dataset for 2005-2009 at MOH. One of
the main findings of those chapters was that most human cases of AI H5N1 infection
resulted from zoonotic, including environmental, transmission of the virus. This
highlights the importance of addressing the disease in the birds and the environment
that can be contaminated with the virus.
One focus for AI H5N1 disease activity is the LBM setting, where live birds come into
the market, are slaughtered and sold to consumers. As was seen in Chapter 4, LBM
exposure was the putative source of infection for seven human cases. LBMs are
considered a site of disease activity at the interface between birds and humans, thus
have the potential for maintaining circulation of virus between birds as well as leading
to human infections. Thus, the subsequent chapters in this PhD examine the
epidemiology and disease control methods in the LBM setting.
The study reported in Chapter 7 identified environmental sites commonly contaminated
by AI H5N1 virus and the risk factors for this contamination. The environmental sites
most commonly contaminated are those in the slaughter and subsequent zones in LBMs.
Slaughtering birds in LBMs was a risk factor for contamination, whilst daily solid waste
removal and clear zoning between work processes such as holding, slaughtering and
selling birds were protective.
My role in the study included designing the study, collecting and analysing the data. I
was also responsible for writing the manuscript and incorporating co-author feedback.
The published study has been reproduced in this chapter with permission from
Emerging Infectious Diseases journal.
89
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Chapter 7
90
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To identify environmental sites commonly contaminat-ed by avian in uenza virus A (H5N1) in live-bird markets in Indonesia, we investigated 83 markets in 3 provinces in Indonesia. At each market, samples were collected from up to 27 poultry-related sites to assess the extent of con-tamination. Samples were tested by using real-time reverse transcription–PCR and virus isolation. A questionnaire was used to ascertain types of birds in the market, general in-frastructure, and work practices. Thirty-nine (47%) markets showed contamination with avian in uenza virus in >1 of the sites sampled. Risk factors were slaughtering birds in the market and being located in West Java province. Protective factors included daily removal of waste and zoning that seg-regated poultry-related work ow areas. These results can aid in the design of evidence-based programs concerning environmental sanitation, food safety, and surveillance to reduce the risk for avian in uenza virus A (H5N1) transmis-sion in live-bird markets.
Food markets that offer both poultry meat and live birds either for sale or for slaughter are collectively referred to as live-bird markets (LBMs). LBMs are part of the sup-ply chain and are essential for maintaining the health and nutritional status of rural and urban populations, especially in developing countries (1,2). However, LBMs provide op-
timal conditions for the zoonotic transfer and evolution of infectious disease pathogens because they provide major contact points between humans and live animals (3,4).
Studies in Hong Kong Special Administrative Region, People’s Republic of China; other areas of China; Indo-nesia; and the United States have shown that LBMs can harbor avian in uenza viruses (AIVs), including highly pathogenic in uenza virus A (H5N1), and have been as-sociated with human infection (4–9). Continual movement of birds into, through, and out of markets provides opportu-nity for the introduction, entrenchment, and dissemination of AIVs. Most studies have focused on testing live birds rather than environmental sites in the LBMs (6,7,10). How-ever, a study in New York, NY, that tested environmental sites for AIV (H7N2) found that virus could be isolated from samples from oors, walls, and drains from the poul-try areas of LBMs (8). The study also found that despite the ongoing in ux of infected birds into LBMs, the level of environmental contamination decreased with routine clean-ing and disinfection. Another study in Hong Kong LBMs showed that AIV (H9N2) could be isolated at higher rates from poultry drinking water than from samples of bird fecal droppings (11). Environmental aspects of LBMs are needed for an avian in uenza control program for 2 reasons. First, a contaminated environment can provide a continuing source of virus transmission, in which healthy birds coming into the market may become infected and persons working in or visiting the market may also be exposed. Second, ongoing surveillance programs in LBMs based on environmental sampling are more likely than those based on invasive bird testing to be acceptable to traders and stall vendors. Envi-ronmental sampling is also safer for public health of cers and veterinary health of cers than handling and sampling live birds that may be infected with AIV.
Environmental Sampling for Avian Infl uenza Virus A (H5N1) in Live-Bird
Markets, IndonesiaRisa Indriani,1 Gina Samaan,1 Anita Gultom, Leo Loth, Sri Indryani, Rma Adjid,
Ni Luh Putu Indi Dharmayanti, John Weaver, Elizabeth Mumford, Kamalini Lokuge, Paul M. Kelly, and Darminto
Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010 1889
Author af liations: Indonesian Research Center for Veterinary Sci-ence, Bogor, Indonesia (R. Indriani, R. Adjid, N.L.P.I. Dharmayanti, Darminto); The Australian National University, Canberra, Australian Capital Territory, Australia (G. Samaan, K. Lokuge, P.M. Kelly); World Health Organization, Jakarta, Indonesia (G. Samaan); Minis-try of Health, Jakarta (A. Gultom, S. Indryani); Food and Agriculture Organization, Dhaka, Bangladesh (L. Loth); Food and Agriculture Organization, Hanoi, Vietnam (J. Weaver); and World Health Orga-nization, Geneva, Switzerland (E. Mumford)
DOI: 10.3201/eid1612.100402 1These authors contributed equally to this article.
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RESEARCH
In this study, we aimed to identify the environmental sites commonly contaminated by AIV (H5N1) in LBMs in Indonesia. Identifying these sites is the rst step in the design of evidence-based environmental sanitation, food safety, and surveillance programs to reduce the risk for vi-rus transmission and to develop environmental surveillance programs to monitor LBM contamination status.
MethodsThree provinces in the western part of Java Island in In-
donesia participated in the study: Jakarta, Banten, and West Java (Figure). Eighteen districts in these provinces were se-lected on the basis of their proximity to the laboratory, high levels of avian in uenza activity in farmed birds (Ministry of Agriculture, unpub. data), and high number of LBMs available for study (n = 300). The required sample size was 73 markets based on an estimated disease prevalence of 50% and a maximum error of 10% at 95% con dence. We based our assumption that 50% of LBMs would be contami-nated with AIV (H5N1) on results from a previous study in US LBMs in 2001 (12). This study found that 60% of mar-kets tested positive for AIV (H7N2) virus in areas in which the virus was endemic. To account for nonresponse, we in-creased the total sample size to 83 LBMs. We selected mar-kets for inclusion in the study using systematic sampling. On the basis of a sampling frame of 300 markets, every fourth market (the sampling interval) was selected from a list of all the markets. A random numbers table was used to determine the starting point for selection of the 83 markets from the list. Diagnostic specimens and data were collected during October 2007–March 2008. These months have high rain-fall and high AIV transmission according to data gathered during 2005–2007 about AIV (H5N1) outbreaks in farmed birds (Ministry of Agriculture, unpub. data).
A structured questionnaire containing 42 questions to assess risk factors for AIV (H5N1) contamination was developed. Responses to questions were obtained through visual inspection of each LBM and through an interview with the manager of the participating LBM. The questions sought information about volume of poultry in the LBM and the infrastructure in the delivery, holding, slaughter, sale, and waste-disposal zones of the market. These 5 zones re ect general demarcation of work ow and activities re-lating to poultry in LBMs (13). Questions about the sanita-tion and slaughtering practices were also included.
Questionnaire validation was conducted by mem-bers of a study advisory team. The team comprised 2 food safety/environmental health of cers from the Ministry of Health, a communicable disease epidemiologist from the World Health Organization, a veterinary epidemiologist from the Food and Agriculture Organization, and 2 virolo-gists from the Ministry of Agriculture in Indonesia. The questionnaire was tested in 3 LBMs in West Java province
to ensure coherence, appropriate use of terminology, and high face validity. The same markets were also inspected to ensure that the questionnaire addressed all aspects of the poultry-related work ow in the 5 poultry zones and rel-evant infrastructure. Members of the study advisory team trained 3 study data collection teams in questionnaire ad-ministration and sample collection procedures.
To select the environmental sites to be sampled in each LBM, the study advisory team visually inspected 3 markets and reviewed the literature to identify LBM sites commonly contaminated with AIVs or similar pathogens. Sites sampled in previous studies for AIV included oors, drains, and water troughs (8,11,12). In this study, 27 sites were selected for environmental sampling (Table 1). The sites represented different poultry-related work activities: 3 sites related to delivery of birds into LBMs, 7 in the bird-holding zone, 9 in the slaughter zone, 6 in the sale zone, and 2 in the waste-disposal zone. Because of variation in LBM infrastructure and processes, each LBM did not nec-essarily have all 27 sites. Samples were collected from as many of the 27 sites as were available in each LBM.
For each of the 27 sites, 6 swab specimens were col-lected and pooled. Each pool (vial) consisted of a maxi-mum of 3 swabs. The data collection teams were instructed to increase the representativeness of the samples by swab-bing different locations for each environmental site. For
1890 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 12, December 2010
Figure. A) Area of study of avian in uenza virus A (H5N1) contamination in live-bird markets (black box), western Java, Indonesia, 2007–2008. B) Distribution of contaminated and uncontaminated markets in the study area.
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Sampling for Avian In uenza
example, if the market had 6 poultry stalls, each with its own scale for weighing poultry, then teams collected 1 swab from each scale and pooled them into 2 pools of 3 swabs each. Swab specimens were pooled in the market, and swabs remained inside the vials until testing. The data collection teams were instructed to focus on visibly dirty, moist, or dif cult-to-clean surfaces in an effort to increase the sensitivity of the sampling.
Sample collection, pooling, transportation, and storage were based on techniques used in previous studies (10,12). Each data collection team comprised 3 persons, 2 of whom collected samples and 1 administered the questionnaire. To reduce the risk for cross-contamination during sample col-lection, teams changed disposable gloves and shoe covers between each of the 5 LBM poultry zones. Sterile cotton-tipped swabs were used to collect all samples, and samples were placed in viral transport media and transported imme-diately back to the laboratory on frozen gel packs. The viral transport media consisted of Dulbecco modi ed Eagle me-dium (Sigma-Aldrich, St. Louis, MO, USA) with 1,000 IU penicillin and gentamicin, and 1% fetal buffer serum (14). Samples were stored in the laboratory at –70°C until tested.
RNA extraction, cDNA synthesis, and real-time re-verse transcription–PCR (RT-PCR) were used as described (15). Vir